Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature

Journal of Biomedical Semantics

Research

Lang Li¹  Weixin Xie¹  Kunjie Fan¹  Shijun Zhang¹ 

[1] Department of Biomedical Informatics, Ohio State University, 43210, Columbus, OH, USA;

关键词: Active learning;    Deep learning;    Drug-drug interaction;    Information retrieval;    Random negative sampling;    Positive sampling;    Similarity sampling;    Uncertainty sampling;   

DOI  :  10.1186/s13326-023-00287-7

 received in 2022-03-09, accepted in 2023-04-29,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDrug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper.ResultsPubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively.ConclusionsBy integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.

【 授权许可】

CC BY   
© The Author(s) 2023

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